#导入仿真库
import tensorflow as tf
import numpy as np

import PIL.Image
from io import StringIO,BytesIO
from IPython.display import clear_output,Image,display

def DisplayFractal(a,fmt='jpeg'):
    #显示迭代计算出的彩色分形图像
    a_cyclic = (6.28*a/20.0).reshape(list(a.shape)+[1])
    img = np.concatenate([10+20*np.cos(a_cyclic),30+50*np.sin(a_cyclic),155-80*np.cos(a_cyclic)],2)

    img[a==a.max()] = 0
    a = img
    a = np.uint8(np.clip(a,0,255))
    f = BytesIO()
    PIL.Image.fromarray(a).save(f,fmt)
    display(Image(data = f.getvalue()))

sess = tf.InteractiveSession()

#Use NumPy to create a 2D array of complex numbers
Y,X = np.mgrid[-1.3:1.3:0.005, -2:1:0.005]
Z = X + 1j*Y

xs = tf.constant(Z.astype("complex64"))

zs = tf.Variable(xs)

ns = tf.Variable(tf.zeros_like(xs,"float32"))

tf.initialize_all_variables().run()

#Compute the new values of z:z^2 + x
zs_ = zs*zs + xs

#Have we diverged with this new value?
not_diverged = tf.abs(zs_) < 4

# Operation to update the zs and the iteration count.
#
# Note: We keep computing zs after they diverge! This
#       is very wasteful! There are better, if a little
#       less simple, ways to do this.

step = tf.group(zs.assign(zs_),ns.assign_add(tf.cast(not_diverged, "float32")))

for i in range(200):
    step.run()

DisplayFractal(ns.eval())